Zhongkai Li

An integrated method for flexible platform modular architecture design

Product platform modular architecture identification is the most critical process to support the incremental design of derivative instances. In order to solve the inflexibility in a single modular or scalable platform of complex mechatronics products, an integrated product modularisation scheme based on flow analysis, design structure matrix (DSM) and fuzzy clustering is proposed to compose a flexible platform. The definition of flexible platform is explained and internal mappings among functions, components and modules are set up. DSM for a kernel product is constructed by the flow analysis between the leaf components in design bill of material. An improved scaling by minimising a convex function algorithm is developed to transform the DSM to vectors in two-dimensional spaces, and the vectors are clustered using the traditional fuzzy c-means algorithm. Cluster centre components are selected to identify modular types with respective functional features. Thus, the internal relationships between components can be modelled with flow analysis-based DSM in a clear format, and the proposed DSM transformation and clustering algorithm identify the modular architecture for a flexible platform in a lower computational complexity. A numerical example and computational comparisons are also given to illustrate the proposed concept and the effectiveness and efficiency of the proposed approach.

Product platform two-stage quality optimization design based on multiobjective genetic algorithm

Product platform design (PFD) has been recognized as an effective means to satisfy diverse market niches while maintaining the economies of scale and scope. Numerous optimization-based approaches have been proposed to help resolve the tradeoff between platform commonality and the ability to achieve distinct performance targets for each variant. In this study, we propose a two-stage multiobjective optimization-based platform design methodology (TMOPDM) for solving the product family problem using a multiobjective genetic algorithm. In the first stage, the common product platform is identified using a nondominated sorting genetic algorithm II (NSGA-II); In the second stage, each individual product is designed around the common platform such that the functional requirements of the product are best satisfied. The design of a family of traction machine is used as an example to benchmark the effectiveness of the proposed approach against previous approachs.

An integrated cultural particle swarm algorithm for multi-objective reliability-based design optimization

Uncertainties in design variables and problem parameters are often inevitable in multi-objective optimizations, and they must be considered in an optimization task if reliable Pareto optimal solutions are to be sought. Multi-objective reliability-based design optimization has been raised as a question in design for reliability, but the disadvantages of fixed evolutionary parameters, nonuniformly distributed Pareto optimal solutions and high computational cost hinder engineering applications of reliability-based design. To deal with it, this work proposes an integrated multi-objective cultural-based particle swarm algorithm to solve the double-loop reliability-based design optimization. In the inner optimization loop, the cultural space is composed of the elitism, situational and normative knowledge to adjust the parameters for swarm space, and the crowding distance ranking is introduced to update the global and local optimum and control the maximum number of solutions in elitism knowledge. The hybrid mean value method is improved to perform reliability analysis in the outer loop to suit both concave and convex types of performance functions. In addition, the car side-impact and the injection molding machine are chosen as multi-objective reliability design examples to demonstrate the effectiveness of the proposed approach. Simultaneously, results of car side-impact problem are compared with two traditional multi-objective reliability optimization algorithms, i.e., nondominated sorting genetic algorithm and crowding distance ranking-based multi-objective particle swarm optimizer, to assess the efficiency of the proposed approach. The results denote the proposed cultural-based multi-objective particle swarm optimizer is effective and feasible to solve the reliability-based design optimization problems.

A methodology to support product platform optimization using multi-objective evolutionary algorithms

A critical step when designing a successful product family is to determine a cost-saving platform configuration along with an optimally distinct set of product variants that target different market segments. A multi-objective optimization-based platform design methodology (MOPDM) was presented to optimize the individual product performances with a feasible platform commonality level. The process and optimization model for scale-based product platform was constructed firstly, and then the MOPDM was carried out in two stages using the non-dominated sorting genetic algorithm II (NSGA-II). A mechanism based on fuzzy set theory was developed to extract one of the Pareto-optimal solutions as the best compromise one. During the first stage of MOPDM, each product in the family was optimized independently with NSGA-II. Those design variables that show small deviations were held constant to form the product platform. The scaling variables of each instance product were optimized in the second stage. The efficiency a...A critical step when designing a successful product family is to determine a cost-saving platform configuration along with an optimally distinct set of product variants that target different market segments. A multi-objective optimization-based platform design methodology (MOPDM) was presented to optimize the individual product performances with a feasible platform commonality level. The process and optimization model for scale-based product platform was constructed firstly, and then the MOPDM was carried out in two stages using the non-dominated sorting genetic algorithm II (NSGA-II). A mechanism based on fuzzy set theory was developed to extract one of the Pareto-optimal solutions as the best compromise one. During the first stage of MOPDM, each product in the family was optimized independently with NSGA-II. Those design variables that show small deviations were held constant to form the product platform. The scaling variables of each instance product were optimized in the second stage. The efficiency and effectiveness of proposed method is illustrated by optimizing a family of six capacitor-run single-phase induction motors, and the results are compared against previous work.

A multi-objective particle swarm optimizer with distance ranking and its applications to air compressor design optimization

Multi-objective particle swarm optimization (MOPSO) has been steadily gaining attention from the research community because of its high convergence speed. On the other hand, in the face of increasing complexity and dimensionality of today’s application coupled with its tendency towards premature convergence related to the high convergence speeds, it is necessary to improve the global convergence and uniform distribution of MOPSO. A novel crowding distance ranking-based particle swarm optimizer is proposed (DMOPSO). With the elitism strategy, the evolution of the external swarm is achieved based on particles’ crowding distance ranking by descending order, to delete the repetitive ones in the crowded area. The update of the global optimum is performed by selecting a particle with relatively bigger crowding distance, to lead the swarm evolving to the disperse region. A small ratio mutation is also introduced to the inner swarm to enhance the global searching capacity of the algorithm. So the number of Pareto optimal solutions can be controlled, and the convergence and diversity of Pareto optimal set can also be guaranteed. The experiment on the optimization of single-stage air compressor showed that DMOPSO handled problems with two and three objectives efficiently, and outperformed the comparison algorithms in terms of the convergence and diversity of the Pareto front. The robustness was illustrated through sensitivity analysis for key parameters.

A systematic adaptable platform architecture design methodology for early product development

ABSTRACT Adaptable product platforms, which express a platforms modular and scalable combinations, are well suited for representing the complex mechanical composition of series products. Current adaptable platform design methods, however, still have certain limitations, such as an insufficient basis for functional modules classification, unstable classified results due to experience-based thresholds, and limited scope because of a reliance on performance mathematical models. Given the hierarchical model of adaptable platform architecture, a two-stage methodology for adaptable platform design based on quantitative indices and fuzzy arithmetic is proposed in this paper. In the first stage, the variety index and change propagation class are applied to cluster the acquired modules into standardised and flexible ones using visualised fuzzy clustering. In the second stage, common and scalable indices based on product lifecycle factors are developed in order to subdivide the components within flexible modules into common and scalable types. Trapezoidal fuzzy arithmetic is introduced in these two stages to deal with the imprecise, approximate, or qualitative linguistic assessments. The proposed approach is demonstrated using the concrete spaying machine family as an exemplary product. Sensitivity analysis showed that the results were robust for the disturbance of design variables. Comparisons with other studies’ results point to a higher stability, lower requirements for basic data, and a general effectiveness of the proposed method.